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Field
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clinical, epidemiological, and immunological data. You should have a PhD, or be close to completion, in bioinformatics, computational biology, microbial genomics or a related discipline, together
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treatment response. The role involves building robust and reproducible computational pipelines, interpreting complex biological data, collaborating across the CRC-STARS consortium, and contributing
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, implementation and evaluation of multi-robot scouting methods suitable for wheeled-legged systems in physically-realistic simulated environments and on real robots. You will hold or be close to completion of a PhD
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framework 3) to develop a minimum viable digital platform prototype to demonstrate scalable CDT delivery. The successful candidate must be able to demonstrate at least one of the following: a PhD degree
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Technicians, Teaching Fellows and AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information. About you: To be successful in this role, we are looking
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progress and outcomes, communicate complex information, orally, in writing and electronically and prepare proposals and applications to external bodies. Applicants will possess a relevant PhD or possess
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-spectrometry-based proteomic and metabolomic workflows, quality control, quantitative data processing, statistical analysis and integration of proteomic and metabolomic datasets. You will also contribute to data
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collection of both quantitative and qualitative data online and in-situ including schools, educational environments, and public/commercial locations About You You will have or to be close to completion of PhD
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://aria.org.uk/opportunity-spaces/resilient-climate-and-ecosystems/accelerated-adaptation for more information on ARIA and this programme). The project is a collaboration between groups at the Universities
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expected to manage your own academic research and administrative activities, adapt existing and develop new scientific techniques and experimental protocols, and test hypotheses and analyse scientific data